code: def meta_ps_roi_align_backward(
      grad,
      rois,
      channel_mapping,
      spatial_scale,
      pooled_height,
      pooled_width,
      sampling_ratio,
      batch_size,
      channels,
      height,
      width,
  ):
      torch._check(
          grad.dtype == rois.dtype,
          lambda: (
              "Expected tensor for grad to have the same type as tensor for rois; "
              f"but type {grad.dtype} does not equal {rois.dtype}"
          ),
      )
      return grad.new_empty((batch_size, channels, height, width))
  
file_path: vision/torchvision/_meta_registrations.py
imports: import functools
  import torch
  import torch._custom_ops
  import torch.library
  import torchvision.extension
label_desc: Use image node_id 6 for calling a global function with example usage: meta_ps_roi_align_backward(grad, rois, channel_mapping, spatial_scale, pooled_height, pooled_width, sampling_ratio, batch_size, channels, height, width) and returns: grad